Author
Fuji Ren
Other affiliations: Hiroshima City University, Beijing University of Posts and Telecommunications, Hefei University ...read more
Bio: Fuji Ren is an academic researcher from University of Tokushima. The author has contributed to research in topics: Sentence & Machine translation. The author has an hindex of 30, co-authored 579 publications receiving 4966 citations. Previous affiliations of Fuji Ren include Hiroshima City University & Beijing University of Posts and Telecommunications.
Papers published on a yearly basis
Papers
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Journal Article•
28 Jan 2008-World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering
TL;DR: This work proposes a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentenceclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries.
Abstract: This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising.
260 citations
TL;DR: This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentenceclusion of name entity, sentence inclusion of numerical data, sentence relative length and aggregated similarity.
235 citations
TL;DR: An up-to-date survey on the sink mobility issue is presented and several representative solutions are described following the proposed taxonomy, to help readers comprehend the development flow within a category.
Abstract: Sink mobility has long been recognized as an efficient method of improving system performance in wireless sensor networks (WSNs), e.g. relieving traffic burden from a specific set of nodes. Though tremendous research efforts have been devoted to this topic during the last decades, yet little attention has been paid for the research summarization and guidance. This paper aims to fill in the blank and presents an up-to-date survey on the sink mobility issue. Its main contribution is to review mobility management schemes from an evolutionary point of view. The related schemes have been divided into four categories: uncontrollable mobility (UMM), path-restricted mobility (PRM), location-restricted mobility (LRM) and unrestricted mobility (URM). Several representative solutions are described following the proposed taxonomy. To help readers comprehend the development flow within the category, the relationship among different solutions is outlined, with detailed descriptions as well as in-depth analysis. In this way, besides some potential extensions based on current research, we are able to identify several open issues that receive little attention or remain unexplored so far.
167 citations
TL;DR: This work uses blogs as object and data source for Chinese emotional expression analysis, and based on this model, a relatively fine-grained annotation scheme is proposed for manual annotation of an emotion corpus.
122 citations
TL;DR: The experimental results show that the proposed class-indexing-based TF.IDF.ICS"@dFterm weighting approach is promising over the compared well-known baseline term weighting approaches.
112 citations
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TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
7,116 citations
Journal Article•
3,940 citations
3,628 citations